Sequence Transfer-Based Particle Swarm Optimization Algorithm for Irregular Packing Problems

• Published in: IEEE access Vol. 9; pp. 131223 - 131235
• Main Authors: Fang, Jie, Rao, Yunqing, Liu, Pan, Zhao, Xusheng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Economic impact; Feature extraction; Heuristic algorithms; Heuristic methods; Industrial applications; Irregular pieces; multiple constraints; Optimization; Optimization algorithms; Packing problem; Particle swarm optimization; particle swarm optimization algorithm; Production costs; sequence transfer; Shape; Task analysis; Transfer learning; two-dimensional packing optimization
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3114331

The two-dimensional (2D) irregular packing problem is a classical optimization problem with NP-hard characteristics and has high computational complexity. To date, packing problems have generally been solved by artificial experience and heuristic algorithms. However, these algorithms are not highly efficient and the excellent cases cannot be preserved, which both time and economic costs are high. Inspire by transfer learning and considering the characteristics of 2D irregular packing problems, we propose a sequence transfer-based particle swarm optimization algorithm (ST-PSO) to solve the multi-constraint packing problem. A piece-matching strategy based on an improved shape context algorithm, and a piece-sequencing generation strategy for transferring the packing sequence are developed for particle swarm optimization(PSO) initialization. In the process of PSO, an adaptive adjustment strategy is used with an improved positioning strategy to adjust the packing position of the pieces. The results indicate that this method can robustly, quickly, and efficiently achieve the packing of 2D irregular pieces. Compared with the data prior to transfer, the ST-PSO can inherit and transfer the historical packing sequence in less time and retain or exceed the actual packing data onto the samples. This algorithm could be applied to industrial applications to reduce waste, packing time, and production costs.